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Data Lakehouse: Next Generation Data Management

Data warehouses have long underpinned business intelligence (BI) analysis based on structured data. This tried-and-trusted approach is now reaching its limits due to growth of unstructured data. Data lakes on the other hand enable vast amounts of unstructured data to be processed using machine learning. But, they can’t support transactions or enforce data quality. Enter data lakehouses: a new hybrid architecture that embodies the best of both worlds.

Date Warehouse + Data Lake = Data Lakehouse

As the name suggests, a data lakehouse essentially combines a data lake and a data warehouse to create an ultramodern data platform that boasts the strengths of both techs. On the one hand, the data warehouse provides clean, structured data. This data is based on relational database schemas and is used primarily for business intelligence (BI) analysis.

The data lake, on the other hand, stores all types of data in any type of format. Unlike the data warehouse, the lake tends not to be deployed for BI tasks due to its lack of validation. Instead, it’s used primarily for data science and machine learning.

From this brief overview, it’s clear that data lakehouses promise the key benefits of data lakes and data warehouses. Not only do they offer low-cost storage in an open format that can be accessed by a wide variety of systems – they also deliver powerful data management and optimization capabilities.

Step by Step to a Winning Hybrid Solution

So, how exactly do you go about putting together a data lakehouse? In a first step, you implement a data lake, providing the necessary low-cost system for storing all your various types of data.

Next, you need to set up a transactional metadata layer on top of the object store. This layer acts as a kind of technological middleman between the unstructured data in the lake and the data user. As such, its job is to categorize, classify, and structure the data.

This combination of data lake and transactional metadata layer allows you to deploy warehouse functionalities like BI analytics on extremely large amounts of unstructured data.

And that’s not all. Because the data lakehouse is built on the lake principle, it also supports machine learning and data science approaches.

Less Admin, Better Data Governance, Greater Cost-Effectiveness

A major benefit of this novel approach is that it significantly reduces administration. Deploying a data lakehouse not only gives you easy access to data in all the sources connected to it; it also consolidates that data for usage. And that means you no longer need to go through the time-consuming chore of extracting information from raw data and then preparing it for processing in the data warehouse.

Another major plus of data lakehouses is that they enhance data governance. By consolidating resources and data sources, a data lakehouse makes for better, more straightforward governance – giving you greater control over security, metrics, role-based access, and other critical management elements.

Last and by no means least, data lakehouses boost cost-effectiveness. Built on an innovative infrastructure that separates computing and storage, the tech allows you to easily add more storage without having to ramp up computing power. As well as enabling you to scale cost-effectively using low-cost data storage, it provides a single solution that allows you to sidestep costly and time-consuming maintenance of multiple data storage systems.

Too Good to Be True?

The appeal of data lakehouses is undeniable. But, as with any novel development, it’s worth remembering that this tech is still relatively new and therefore immature. As a result, it’s too early to confidently predict whether it will deliver on its promises. In fact, it could take years for data lakehouses to reach the stage where they can go head-to-head with proven big-data

storage solutions.

That being said, the data lakehouse concept is highly promising and is a real paradigm shift in data platform architecture. By enabling organizations not only to store both structured and unstructured data, but also to link and analyze it in a single manageable system, the tech

could be an invaluable asset when it comes to tackling the tasks of the future.

Interested in Learning More About BI Solutions?

As companies face the dual challenge of accelerating their decision-making while drawing on an ever-growing pool of data, finding the right business intelligence solutions is imperative. And data lakehouses are just one of the fascinating developments in today’s BI and analytics space.

If you’re interested in finding out more about data lakehouses and related technologies, feel free to reach out to me. And if you have your own thoughts on data lakehouses and their potential, why not share them in the comments section below?

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